Abstract
Abstract. In this study, we investigate a strategy to accelerate the data assimilation (DA) algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study of the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. This will reduce the computational cost in the analysis step. Experimental results show the mask-state algorithm significantly speeds up the analysis step. Subsequently, the total amount of computing time for volcanic ash DA is reduced to an acceptable level. The mask-state algorithm is generic and thus can be embedded in any ensemble-based DA framework. Moreover, ensemble-based DA with the mask-state algorithm is promising and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change in the full model.
Highlights
Volcanic ash erupted into atmospheres can lead to severe influences on aviation society (Gudmundsson et al, 2012)
mask-state algorithm (MS) will be applied in the real volcanic ash data assimilation (DA) system, to investigate whether in practice it can speed up the analysis step well
The only difference between MS-ensemble Kalman filter (EnKF) and conventional EnKF is that in the former MS is employed for the analysis step, and in the latter is the standard analysis step
Summary
Volcanic ash erupted into atmospheres can lead to severe influences on aviation society (Gudmundsson et al, 2012). Accurate real-time aviation advice is highly required during an explosive volcanic ash eruption (Eliasson et al, 2011). Using data assimilation (DA) to improve model forecast accuracy is a powerful approach (Lu et al, 2016a). Ensemble-based DA (Evensen, 2003) has been evaluated as very useful for improving volcanic ash forecasts and regional aviation advice (Fu et al, 2016). It corrects volcanic ash concentrations by continuously assimilating observations. Based on the validation with independent data, ensemble-based DA was concluded as being powerful for improving the forecast accuracy
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call